1

Hydrobiologia

Spatio-temporal organization patterns in the fish assemblages of a Neotropical floodplain

Jean C. G. Ortega1*, Rosa M. Dias1, Ana C. Petry2, Edson F. Oliveira3, Angelo A. Agostinho4

1Programa de Pós-Graduação em Ecologia de Ambientes Aquáticos Continentais, Universidade Estadual de Maringá, Av. Colombo, 5.790,

Bloco H-90, Laboratório de Ictiologia. CEP: 87020-900 Maringá, PR, Brasil.

2Núcleo em Ecologia e Desenvolvimento Sócio-Ambiental de Macaé, Universidade Federal do Rio de Janeiro, Campus Macaé, Av. São José do Barreto, 764, Bairro São José do Barreto. CEP: 27971-550 Macaé, RJ, Brasil.

3Programa de Pós-Graduação em Engenharia Ambiental, Universidade Tecnológica Federal do Paraná, Campus Londrina, Av. dos Pioneiros,

3131. CEP: 86036-370 Londrina, PR, Brasil.

4Núcleo de Pesquisas em Limnologia, Ictiologia e Aquicultura, Universidade Estadual de Maringá

*Corresponding author: [email protected], +55 44 3011-4607 2

Online Resource 1 Physical and chemical variables of the water measured from March 2000 to December 2012 in various environments of the upper Paraná River floodplain. Mean ± standard deviation. Site Sub-basin Type of Temperatur Transparency (cm) pH Conductivity environment e (°C) (µS/cm) Fechada Lagoon Baia River Closed lagoon 25.43 ± 3.98 44.2 ± 30.53 6.34 ± 0.55 28.67 ± 8.68 Guaraná Lagoon Baia River Open lagoon 25.27 ± 3.93 67.79 ± 42.65 6.16 ± 0.43 34.22 ± 8.46 Baia River Baia River River channel 25.27 ± 4.06 86.3 ± 30.78 6.55 ± 0.45 30.45 ± 8.79 Patos Lagoon Ivinhema River Open lagoon 25.72 ± 4.36 59.09 ± 36.52 6.62 ± 0.5 38.56 ± 7.01 Ventura Lagoon Ivinhema River Closed lagoon 25.21 ± 4.38 40.69 ± 35.75 6.8 ± 0.36 42.95 ± 9.13 Ivinhema River Ivinhema River River channel 25.01 ± 4 65.35 ± 20.62 6.85 ± 0.34 42.85 ± 5.24 Garças Lagoon Paraná River Open lagoon 26.41 ± 3.72 103.08 ± 68.58 6.96 ± 0.4 54.37 ± 5.19 Osmar Lagoon Paraná River Closed lagoon 23.92 ± 4.41 83.46 ± 45.21 6.28 ± 0.4 48.52 ± 9.95 Pau Véio Backwater Paraná River Open lagoon 25.4 ± 3.68 189.96 ± 73.45 6.82 ± 0.36 57.61 ± 5.29 Paraná River Paraná River River channel 24.56 ± 3.45 259.44 ± 102.36 7.16 ± 0.33 61.31 ± 3.08 3

Continuation. Site Dissolved Chlorophyll a Total nitrogen Total phosphorus oxigen (ml/L) (χ) (μg/L-1) (μg/L-1) Fechada Lagoon 5.71 ± 1.81 17.17 ± 21.73 1093.28 ± 601.62 101.71 ± 50.78 Guaraná Lagoon 4.82 ± 2.12 10.1 ± 7.41 936.24 ± 580.62 59.71 ± 49.47 Baia River 6.39 ± 1.59 6.55 ± 6.21 618.45 ± 294.77 38.58 ± 14.64 Patos Lagoon 5.33 ± 2.53 9.17 ± 9.11 730.23 ± 397.54 49.85 ± 21.3 Ventura Lagoon 6.44 ± 1.43 11.39 ± 13.3 1008.03 ± 533.53 70.26 ± 34.55 Ivinhema River 6.58 ± 1.38 1.55 ± 2.2 552.65 ± 329.94 41.7 ± 15.38 Garças Lagoon 6.72 ± 1.25 9.15 ± 8.21 497.52 ± 246.1 37.46 ± 41.11 Osmar Lagoon 3.93 ± 1.83 11.36 ± 9.22 729.34 ± 351.02 68.13 ± 46.59 Pau Véio Backwater 6.45 ± 1.09 4.01 ± 4.31 445.26 ± 255.72 17.15 ± 7.22 Paraná River 7.25 ± 0.74 1.71 ± 2.72 467.64 ± 246.62 11.14 ± 6.62 4

Online Resource 2 Additional results.

Each sampled specimen had its total length measured to the nearest centimeter.

To assess if the maximum total length of the species sampled by the gill nets and seines differed, a one-way analysis of variance was performed. The assumptions of homogeneity of the variances and normality of the residuals were met after the total length was transformed by log10. This analysis was performed because these fishing gears select for size (Olin & Malinen, 2003), and the fish assemblages are often structured by length (Matthews, 1998); thus, a probable overall difference in body size can help to explain different co-occurrence patterns.

The maximum length varied between 1.5 to 299.9 cm (mean ± standard deviation: 34.72 ± 37.52 cm). The maximum length of the species sampled by gill nets was, on average, 2.81 times greater than that of the species sampled by seines (F1, 196

= 88.45, p< 0.01).

References

Matthews, W. J., 1998. Patterns in freshwater fish ecology. Kluwer Academic

Publishers.

Olin, M. &T. Malinen, 2003. Comparison of gillnet and trawl in diurnal fish community

sampling. Hydrobiologia 506-509: 443-449. 5

Online Resource 3

Script of analyses conducted in the software R.

install.packages(“vegan”)

####C-Score calculation and null models#### pm00#”pm00” is a presence/absence matrix in which lines are the species and columns #are sampling sites or periods cs.pm00<- oecosimu(pm00[1:59,1:9],nestedchecker,statistic="C.score","swap",nsimul=30000)

#C-Score calculation, randomization algorithm and number of simulations by matrix cs.pm00#Summary of observed C-Score, mean of the simulated, p-value and the

#C-Score standardized effect size (SES) pm00.scores<-cs.pm00$oecosimu$simulated[,1:30000] sd(pm00.scores)#Standard deviation of the simulated C-Scores

#We run this procedure for each matrix assessed (general pattern, spatial and temporal #patterns) both for gillnet and seine data

####Principal ComponentAnalysis (PCA) on physical and chemical variables measured#### abiotics<-read.table("abiotics.txt",h=T)#”abiotics” is a data frame where the columns are #physical and chemical variables abio.pca<-cbind(log10(abiotics$Water_temp+1),log10(abiotics$Secchi+1), abiotics$pH,log10(abiotics$Conductivity+1),log10(abiotics$Dissolved_oxigen+1), log10(abiotics$Chlorophyll_a+1),log10(abiotics$Total_nitrogen+1), log10(abiotics$Total_phosphorus+1)) colnames(abio.pca)<-c("Tagu","Tran","pH","Cond","Oxig","Clor","Ntot","Ftot")

#Correlation PCA: pca.abiot<-prcomp(abio.pca,scale.=T)#Correlation PCA 6 scores<-pca.abiot$x#PCA scores eigenvalues<-apply(scores,2,var)#PCA eigenvalues

(eigenvalues/sum(eigenvalues))*100#Percentage of explanation byeach axis round(eigenvalues,digits=3)#We retained to interpretation all axes with eigenvalue

#greater than 1 t(round(t(pca.abiot$rotation)*pca.abiot$sdev,3))#Eigenvectors

####Multiple linear regressions between SES values and the scores of the three first

#PCA axes#### x<-read.table(file="clipboard",sep="\t",h=T)#”x” is a data frame in which the three first

#columns are sampling gear, year and sampling month, the 4thare theSES values and

#from the 5th to 7th are PCA scores. x x.seine<-subset(x,Fish_Gear=='Seine') x.gill<-subset(x,Fish_Gear=='Gillnet') lm.ses.gill<-lm(x.gill$SES~x.gill$PCA1+x.gill$PCA2+x.gill$PCA3)#Multiple linear

#regression lm.ses.seine<-lm(x.seine$SES~x.seine$PCA1+x.seine$PCA2+x.seine$PCA3) summary(lm.ses.rede);summary(lm.ses.arr)

####Simple linear regressions between SES values and the river level #### high<-read.table(file="clipboard",sep="\t",h=T)#”high” is a data frame in which the two

#first columns are year, month, the 3rd is the maximum river level, the 4th is gillnet SES

#values and the 5th is seine SES values (for sampling months with river level above

#460cm) low<-read.table(file="clipboard",sep="\t",h=T)#”low” is a data frame in which the two first #columns are year, month, the 3rd is maximum river level, the 4th is gillnet SES 7 values #and the 5th is seine SES values (for sampling months with river level below

460cm) ra<-lm(high$SES.gill~high$Max_RL)#Regression between SES and river level for high

#water periods (gillnet) aa<-lm(high$SES.seine~high$Max_RL)#Regression between SES and river level for

#high water periods (seine) rb<-lm(low$SES.gill~low$Max_RL)#Regression between SES and river level for low

#water periods (gillnet) ab<-lm(low$SES.seine~low$Max_RL) #Regression between SES and river level for low #water periods (seine) summary(ra);summary(rb) summary(aa);summary(ab)

####Analysis of variance between SES and years#### ses<-read.table("SES_periodo.txt",head=T)#”ses” is a data frame in which the two first

#columns are year, month, the 3rd is SES values for gillnet and the 4th is SES values for

#seine aov.ses.gill<-aov(ses$SES.gill~as.factor(ses$Year)) aov.ses.seine<-aov(ses$SES.seine~as.factor(ses$Year)) summary(aov.ses.gill);summary(aov.ses.seine)

#Test of assumptions: qqnorm(aov.ses.gill$residuals) qqline(aov.ses.gill$residuals) bartlett.test(ses$SES.gill~ses$Year) qqnorm(aov.ses.seine$residuals) qqline(aov.ses.seine$residuals) bartlett.test(ses$SES.seine~ses$Year)

#Final ANOVAs: 8 aov.ses.gill<-lm(ses$SES.gill~ses$Year)#one-way ANOVA for gillnet summary(aov.ses.rede) oneway.test(ses$SES.seine~ses$Year)#one-way ANOVA assuming heterogeneous

#variances for seine 9

Online Resource4 List of the species sampled in the upper Paraná River floodplain from March 2000 to

December 2012.

CHONDRICHTHYES MYLIOBATIFORMES POTAMOTRYGONIDAE Potamotrygon cf. falkneri Castex & Maciel, 1963 Potamotrygon cf. motoro (Müller & Henle, 1841) Potamotrygon sp. OSTEICHTHYES CHARACIFORMES PARODONTIDAE Apareiodon affinis (Steindachner, 1879) Parodon nasus Kner, 1859 CURIMATIDAE Cyphocharax modestus (Fernández-Yépez, 1948) Cyphocharax nagelii (Steindachner, 1881) Steindachnerina brevipinna (Eigenmann & Eigenmann, 1889) Steindachnerina insculpta (Fernández-Yépez, 1948) PROCHILODONTIDAE Prochilodus lineatus (Valenciennes, 1836) ANOSTOMIDAE Leporellus vittatus (Valenciennes, 1850) Leporinus elongatus Valenciennes, 1850 Leporinus friderici (Bloch, 1794) Leporinus lacustris Campos, 1945 Leporinus macrocephalus Garavello & Britski, 1988 Leporinus obtusidens (Valenciennes, 1836) Leporinus octofasciatus Steindachner, 1915 Schizodon altoparanae Garavello & Britski, 1990 Schizodon borellii (Boulenger, 1900) Schizodon nasutus Kner, 1858 CRENUCHIDAE Characidium aff. zebra Eigenmann, 1909 Characidium sp. HEMIODONTIDAE Hemiodus orthonops Eigenmann & Kennedy, 1903 CHARACIDAE Astyanax altiparanae Garutti & Britski, 2000 Astyanax aff. fasciatus (Cuvier, 1819) Astyanax schubarti Britski, 1964 Bryconamericus stramineus Eigenmann, 1908 Hemigrammus marginatus Ellis, 1911 10

Hyphessobrycon eques (Steindachner, 1882) Hyphessobrycon aff. iheringi Fowler, 1941 Hyphessobrycon sp. Moenkhausia bonita Benine, Castro & Sabino, 2004 Moenkhausia gracilima Eigenmann, 1908 Moenkhausia aff. intermedia Eigenmann, 1908 Moenkhausia aff. sanctaefilomenae (Steindachner, 1907) Oligosarcus pintoi Campos, 1945 Psellogrammus kennedyi (Eigenmann, 1903) Salminus brasiliensis (Cuvier, 1816) Salminus hilarii Valenciennes, 1850 BRYCONINAE Brycon orbignyanus (Valenciennes, 1850) SERRASALMINAE Colossoma macropomum (Cuvier, 1818) Metynnis lippincottianus (Cope, 1870) Myleus tiete (Eigenmann & Norris, 1900) Piaractus mesopotamicus (Holmberg, 1887) Serrasalmus maculatus Kner, 1858 Serrasalmus marginatus Valenciennes, 1837 APHYOCHARACINAE Aphyocharax anisitsi Eigenmann & Kennedy, 1903 Aphyocharax dentatus Eigenmann & Kennedy, 1903 Aphyocharax sp. CHARACINAE Galeocharax knerii (Steindachner, 1879) Roeboides descalvadensis Fowler, 1932 CHEIRODONTINAE Odontostilbe sp. Serrapinnus notomelas (Eigenmann, 1915) Serrapinnus sp. 1 Serrapinnus sp. 2 ACESTRORHYNCHIDAE Acestrorhynchus lacustris (Lütken, 1875) Acestrorhynchus pantaneiro Menezes, 1992 CYNODONTIDAE Rhaphiodon vulpinus Spix & Agassiz, 1829 ERYTHRINIDAE Erythrinus (Bloch & Schneider, 1801) Hoplerythrinus unitaeniatus (Agassiz, 1829) Hoplias sp. 1 Hoplias sp. 2 Hoplias sp. 3 LEBIASINIDAE 11

Pyrrhulina australis Eigenmann & Kennedy, 1903 SILURIFORMES CALLICHTHYIDAE Callichthys (Linnaeus, 1758) Hoplosternum littorale (Hancock, 1828) Lepthoplosternum pectorale (Boulenger, 1895) CLARIIDAE Clarias gariepinus (Burchell, 1822) LORICARIIDAE LORICARIINAE Loricaria sp. Loricariichthys platymetopon Isbrücker & Nijssen, 1979 Loricariichthys rostratus Reis & Pereira, 2000 HYPOSTAMINAE Hypostomus ancistroides (Ihering, 1911) Hypostomus cochliodon Kner, 1854 Hypostomus commersoni Valenciennes, 1836 Hypostomus microstomus Weber, 1987 Hypostomus regani (Ihering, 1905) Hypostomus cf. strigaticepis (Regan, 1908) Hypostomus ternetzi (Boulenger, 1895) Megalancistrus paranamus (Peters, 1881) Pterygoplichthys ambrosettii (Holmberg, 1893) Rhinelepis aspera Spix & Agassiz, 1829 HEPTAPTERIDAE Pimelodella avanhandavae Eigenmann, 1917 Pimelodella gracilis (Valenciennes, 1835) Pimelodella taenioptera Miranda-Ribeiro, 1914 Pimelodella sp. Rhamdia quelen (Quoy & Gaimard, 1824) PIMELODIDAE Hemisorubim platyrhynchos (Valenciennes, 1840) Hypophthalmus edentatus Spix & Agassiz, 1829 Iheringichthys labrosus (Lütken, 1874) Pimelodus cf. argenteus Perugia, 1891 Pimelodus maculatus La Cepède, 1803 Pimelodus microstoma Steindachner, 1877 Pimelodus mysteriosus Azpelicueta, 1998 Pimelodus ornatus Kner, 1858 Pinirampus pirinampu (Spix & Agassiz, 1829) Pseudoplatystoma corruscans (Spix & Agassiz, 1829) Sorubim lima (Bloch & Schneider, 1801) Zungaru jahu DORADIDAE 12

Ossancora eigenmanni (Boulenger, 1895) Platydoras armatulus (Valenciennes, 1840) Pterodoras granulosus (Valenciennes, 1821) Rhinodoras dorbignyi (Kner, 1855) Trachydoras paraguayensis (Eigenmann & Ward, 1907) AUCHENIPTERIDAE Ageneiosus inermis (Linnaeus, 1766) Ageneiosus militaris Valenciennes, 1836 Ageneiosus ucayalensis Castelnau, 1855 Auchenipterus osteomystax (Miranda-Ribeiro, 1918) Trachelyopterus galeatus (Linnaeus, 1766) GYMNOTIFORMES GYMNOTIDAE Gymnotus inaequilabiatus (Valenciennes, 1839) Gymnotus paraguensis Albert & Crampton, 2003 Gymnotus sylvius Albert & Fernandes-Matioli, 1999 STERNOPYGIDAE Eigenmannia trilineata López & Castello, 1966 Eigenmannia virescens (Valenciennes, 1836) Sternopygu macrurus (Bloch & Schneider, 1801) RHAMPHICHTHYIDAE Rhamphichthys hahni (Meinken, 1937) HYPOPOMIDAE Brachyhypopomus cf. pinnicaudatus (Hopkins, Comfort, Bastian & Bass, 1990) APTERONOTIDAE Apteronotus ellisi (Alonso de Arámburu, 1957) Apteronotus sp. CYPRINODONTIFORMES RIVULIDAE Melanorivulus apiamici (Costa, 1989) POECILIIDAE Pamphorichthys sp. Poecilia reticulata Peters, 1859 SYNBRANCHIFORMES SYNBRANCHIDAE Synbranchus marmoratus Bloch, 1795 PERCIFORMES SCIANIDAE Plagioscion squamosissimus (Heckel, 1840) CICHLIDAE Astronotus crassipinnis (Heckel, 1840) Apistogramma commbrae (Regan, 1906) Cichla kelberi Kullander & Ferreira, 2006 13

Cichla piquiti Kullander & Ferreira, 2006 Cichla sp. Cichlasoma paranaense Kullander, 1983 Crenicichla britskii Kullander, 1982 Crenicichla haroldoi Luengo & Britski, 1974 Crenicichla niederleinii (Holmberg, 1891) Geophagus cf. proximus (Castelnau, 1855) Laetacara araguaiae Ottoni & Costa, 2009 Satanoperca pappaterra (Heckel, 1840) PLEURONECTIFORMES ACHIRIDAE Catathyridium jenynsii (Günther, 1862) 14

Online Resource 5 Observed and simulated C-Score, standard deviation (s), C-Score standard effect size

(SES), significance level (p) and the number of species (N) for each model of the gillnet matrices.

C- C-

Models Scoreobs Scoresim s SES p N 11

Whole floodplain 139.952 137.420 0.296 8.566 <0.001 8 Spatial scale 10

Sub-basin Baia River 111.147 108.610 0.289 8.774 <0.001 7 Sampling Fechada Lagoon 69.777 68.022 0.330 5.325 <0.001 74 site Guaraná Lagoon 75.401 74.383 0.409 2.487 0.016 79 Baia River Channel 127.606 126.240 0.416 3.281 <0.001 95 11

Ivinhema River 121.972 120.100 0.291 6.434 <0.001 4 Patos Lagoon 101.799 99.099 0.447 6.042 <0.001 87 Ventura Lagoon 82.810 81.392 0.418 3.389 0.002 79 Ivinhema River

Channel 129.337 127.560 0.494 3.599 <0.001 99 11

Paraná River 125.626 122.510 0.318 9.794 <0.001 5 Garças Lagoon 85.278 83.801 0.276 5.354 <0.001 73 Pau Véio Backwater 63.024 62.344 0.367 1.853 0.086 72 Paraná River 10

Channel 123.180 121.780 0.362 3.871 <0.001 1 Type of

environmen

t 10

Open Lagoon 103.583 102.400 0.199 5.914 <0.001 2 Closed Lagoon 89.327 87.441 0.289 6.515 <0.001 89 12

River Channel 175.899 172.620 0.360 9.121 <0.001 6 Temporal scale 11

Season Autumn 141.993 139.830 0.293 7.384 <0.001 5 Winter 125.544 123.690 0.323 5.749 <0.001 10 15

9 11

Spring 122.844 119.500 0.334 10.018 <0.001 2 11

Summer 156.259 153.760 0.382 6.535 <0.001 0 Year 2000 89.894 88.107 0.387 4.619 <0.001 73 Month March 121.556 119.130 0.919 2.642 0.029 59 June 99.833 97.806 1.033 1.961 0.083 54 September 52.194 51.431 0.599 1.275 0.227 39 December 89.139 88.620 1.090 0.476 0.544 55 2001 97.230 95.218 0.444 4.534 <0.001 71 March 83.250 81.938 0.761 1.725 0.116 50 June 111.417 108.490 0.816 3.581 0.004 56 September 95.861 93.567 0.850 2.698 0.023 52 December 106.778 104.790 0.980 2.026 0.079 56 2002 134.006 131.030 0.441 6.747 <0.001 87 March 121.944 117.140 1.276 3.764 0.004 68 June 124.944 123.020 0.938 2.047 0.070 59 September 116.694 116.840 1.043 -0.142 0.977 59 December 165.917 164.890 1.273 0.803 0.397 72 2003 114.392 111.710 0.666 4.025 0.001 71 March 122.778 114.130 1.267 6.822 <0.001 62 September 100.750 100.800 0.719 -0.069 1 50 2004 116.681 114.850 0.608 3.014 0.006 89 March 134.889 133.400 1.031 1.442 0.185 63 June 100.667 99.233 0.988 1.451 0.170 55 September 110.361 108.410 0.997 1.959 0.080 56 December 124.417 120.300 1.511 2.724 0.020 68 2005 142.667 137.810 0.595 8.156 <0.001 92 March 146.667 141.640 1.420 3.542 0.008 72 June 121.972 120.870 0.905 1.214 0.242 61 September 99.528 95.835 1.074 3.438 0.008 57 December 169.222 163.930 1.328 3.982 0.001 75 2006 146.249 141.980 0.472 9.053 <0.001 88 March 151.111 149.740 1.058 1.298 0.215 62 June 152.333 146.850 1.034 5.300 <0.001 67 September 140.111 134.180 0.918 6.461 <0.001 60 December 146.306 143.920 1.080 2.204 0.063 63 2007 146.497 141.470 0.549 9.155 <0.001 91 March 83.611 83.936 0.854 -0.381 0.775 57 June 129.472 127.950 0.941 1.619 0.133 65 September 153.944 147.740 1.235 5.023 0.002 70 December 173.444 163.570 1.651 5.979 <0.001 76 2008 134.008 130.350 0.590 6.199 <0.001 91 March 140.750 140.100 1.047 0.617 0.491 64 16

June 114.000 111.630 1.081 2.197 0.053 59 September 116.333 112.880 1.005 3.439 0.006 58 December 146.972 144.480 1.578 1.578 0.138 70 2009 124.202 120.630 0.552 6.466 <0.001 87 March 120.194 114.670 1.031 5.360 <0.001 61 June 84.417 82.832 0.902 1.757 0.107 50 September 134.167 131.430 1.013 2.699 0.020 62 December 158.222 157.310 1.249 0.729 0.432 72 2010 158.889 152.330 0.547 11.990 <0.001 97 March 141.917 138.810 1.160 2.674 0.029 73 June 117.500 114.590 1.082 2.684 0.030 65 September 157.778 151.410 1.227 5.187 <0.001 72 December 149.417 138.320 1.033 10.745 <0.001 70 2011 134.659 130.670 0.533 7.484 <0.001 92 March 79.861 79.874 1.243 -0.010 0.917 55 June 113.306 107.980 1.166 4.570 0.002 66 September 131.250 126.100 1.090 4.723 0.001 65 December 133.083 131.130 1.438 1.360 0.196 72 2012 130.337 125.780 0.482 9.451 <0.001 81 March 140.036 132.710 1.069 6.857 <0.001 63 June 110.929 111.060 0.969 -0.138 1 56 September 136.893 131.780 1.231 4.154 0.003 63 December 138.786 137.430 1.034 1.308 0.218 63 17

Online Resource 6 Observed and simulated C-Score, standard deviation (s), C-Score standard effect size

(SES), significance level (p) and the number of species (N) for each model of the seine matrices.

C- C-

Models Scoreobs Scoresim s SES p N 0.67 6 Whole floodplain 31.147 31.094 0.107 0.492 0 5 Spatial scale 0.35 6

Sub-basin Baia River 31.817 31.637 0.193 0.932 2 6 0.02 5

Sampling site Fechada Lagoon 40.044 39.302 0.314 2.364 3 9 0.76 5

Guaraná Lagoon 23.912 23.849 0.225 0.279 1 3 0.01 8

Ivinhema River 36.065 35.348 0.210 3.419 0 1 0.00 6

Patos Lagoon 34.831 33.742 0.295 3.688 2 5 0.06 6

Ventura Lagoon 33.974 33.569 0.214 1.898 3 2 0.10 7

Paraná River 21.684 21.465 0.134 1.632 2 0 0.38 4

Garças Lagoon 18.199 18.025 0.203 0.855 7 6 Pau Véio 0.53 4

Backwater 13.752 13.860 0.171 -0.630 0 2 0.17 5

Osmar Lagoon 24.329 23.927 0.276 1.455 2 1 Type

ofenvironment 0.05 8

Open Lagoon 25.997 25.787 0.105 1.998 8 9 0.14 8

Closed Lagoon 36.190 35.921 0.173 1.555 8 3 Temporal scale Season 18

0.30 7

Autumn 24.712 24.490 0.205 1.080 4 3 0.15 6

Winter 33.477 33.178 0.197 1.513 3 9 0.62 6

Spring 27.548 27.659 0.200 -0.559 7 7 0.88 8

Summer 38.332 38.297 0.251 0.141 9 2 0.54 5

Year 2002 28.581 28.378 0.371 0.548 7 3 0.15 1

Month March 11.400 10.740 0.404 1.635 4 7 0.87 4

June 40.238 40.500 0.804 -0.326 9 1 0.28 2

September 23.238 22.596 0.578 1.111 1 8 1.00 3

December 40.381 40.484 1.049 -0.098 0 7 0.83 3

2003 20.269 20.384 0.378 -0.304 4 0 0.59 2

March 34.133 34.528 0.588 -0.671 4 8 0.52 1

September 10.619 11.048 0.552 -0.777 0 9 0.22 4

2004 17.023 16.730 0.232 1.263 6 0 0.26 2

March 30.133 28.970 0.992 1.172 5 9 0.78 2

June 15.533 15.420 0.440 0.258 4 0 0.83 2

September 14.952 15.126 0.477 -0.364 4 3 0.31 1

December 16.467 16.900 0.401 -1.080 5 9 0.20 5

2005 27.678 27.104 0.428 1.341 1 1 March 32.000 32.458 0.128 -3.580 0.83 3 19

0 8 0.03 2

June 22.524 21.033 0.548 2.722 1 6 0.06 3

September 29.667 31.335 1.094 -1.524 4 3 0.34 2

December 16.533 17.159 0.648 -0.965 5 4 0.65 3

2006 19.353 19.453 0.208 -0.480 7 8 0.47 1

March 11.000 11.369 0.436 -0.846 9 9 0.19 2

June 17.714 18.306 0.465 -1.271 0 2 0.08 2

September 14.000 14.734 0.496 -1.479 4 1 0.24 2

December 35.095 35.716 0.555 -1.118 9 9 0.26 5

2007 28.172 27.795 0.334 1.131 3 1 0.46 1

March 9.000 8.761 0.346 0.690 2 6 0.93 3

June 40.133 40.150 0.963 -0.017 8 1 0.26 2

September 13.333 12.729 0.504 1.200 5 2 0.53 3

December 50.810 50.421 0.679 0.573 9 5 0.28 5

2008 27.903 27.492 0.382 1.075 5 2 0.72 3

March 27.476 27.798 0.682 -0.472 6 2 0.55 2

June 12.476 13.038 0.839 -0.669 9 7 0.25 2

September 24.733 25.522 0.702 -1.123 9 5 0.42 3

December 48.714 47.965 0.966 0.776 1 7 20

0.42 4

2009 19.903 20.130 0.279 -0.813 4 8 0.72 3

March 25.476 25.852 0.791 -0.475 4 2 0.60 2

June 22.571 22.896 0.507 -0.640 1 5 0.12 1

September 10.800 10.138 0.374 1.770 5 8 0.64 3

December 20.381 20.121 0.791 0.328 8 1 0.20 5

2010 30.735 31.109 0.299 -1.251 5 2 0.89 1

March 11.000 11.133 0.349 -0.381 8 7 0.53 3

June 43.857 44.458 0.862 -0.697 4 4 0.78 3

September 43.381 43.642 0.632 -0.413 6 4 0.95 2

December 24.571 24.582 0.587 -0.018 6 5 0.14 4

2011 30.638 30.196 0.286 1.544 6 7 1.00

March 1.667 1.667 0.000 0.000 0 4 0.32 2

June 24.714 25.276 0.568 -0.990 0 8 0.96 3

September 48.905 49.073 0.792 -0.212 4 6 0.10 3

December 35.857 34.579 0.690 1.853 1 3 0.94 5

2012 37.473 37.481 0.345 -0.023 3 5 0.18 3

March 32.762 31.844 0.645 1.424 3 1 0.38 3

June 39.286 38.519 0.876 0.876 2 4 21

0.60 2

September 28.667 28.308 0.720 0.498 1 9 0.81 4

December 53.524 53.899 1.075 -0.349 3 2